Noise and texture tuning in digital image sensors is required to correctly calibrate the multitude of parameters that control chrominance and luminance noise frequencies. There are different sources of noise in digital images acquired by image sensors in digital cameras, camcorders, and scanners, including fixed-pattern noise and temporal noise. Many factors determine the overall noise characteristics in an image: sensor type, pixel dimensions, temperature, exposure time, etc. Noise can also vary within an individual image. For digital cameras, darker regions may contain more noise than the brighter regions. Moreover, noise is space varying and channel dependent. The blue channel is often the noisiest channel. Classical noise-reduction techniques remove noise from the Bayer image, before the color interpolation step. Thus, classical noise reduction techniques assume the noise to be uncorrelated for different pixels.
The amount of noise which is not removed by the noise reduction technique is often spread in a neighborhood by the color-interpolation algorithm, which infers missing color components. Consequently, noise may have low-frequency (coarse-grain) and high-frequency (fine-grain) variations. High-frequency noise is relatively easier to remove than low-frequency noise, which may be difficult to distinguish from the real image signal. Moreover, noise is composed of two elements: fluctuations in color and luminance. Color or “chroma” noise is usually more unnatural in appearance than luminance noise, and can render images unusable if the image sensor is incorrectly calibrated. This kind of noise may appear as low-frequency, colored blobs in regions of low spatial frequency. These colored blobs may be irregularly shaped and are typically around 5 to 25, or more, pixels wide in a given direction, and usually are more pronounced in darker regions than in brighter regions.
Existing approaches for noise and texture tuning in digital cameras and devices that include digital cameras (e.g., mobile phones), typically involve considering more than hundreds of parameters, as well as manual tuning methods. Manual tuning methods may be time-consuming and based on subjective evaluation by human operators.